| BoTorch: a framework for efficient Monte-Carlo Bayesian optimization M Balandat, B Karrer, D Jiang, S Daulton, B Letham, AG Wilson, E Bakshy Advances in neural information processing systems 33, 21524-21538, 2020 | 251* | 2020 |
| Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization S Daulton, M Balandat, E Bakshy Advances in Neural Information Processing Systems 33, 9851-9864, 2020 | 61 | 2020 |
| Contract design for frequency regulation by aggregations of commercial buildings M Balandat, F Oldewurtel, M Chen, C Tomlin 2014 52nd Annual Allerton Conference on Communication, Control, and …, 2014 | 51 | 2014 |
| Residential demand response targeting using machine learning with observational data D Zhou, M Balandat, C Tomlin 2016 IEEE 55th conference on decision and control (CDC), 6663-6668, 2016 | 43 | 2016 |
| On infinite horizon switched LQR problems with state and control constraints M Balandat, W Zhang, A Abate Systems & Control Letters 61 (4), 464-471, 2012 | 30 | 2012 |
| Constrained robust optimal trajectory tracking: Model predictive control approaches M Balandat Control Systems Technology, 2010 | 28 | 2010 |
| Building model identification during regular operation-empirical results and challenges Q Hu, F Oldewurtel, M Balandat, E Vrettos, D Zhou, CJ Tomlin 2016 American Control Conference (ACC), 605-610, 2016 | 23 | 2016 |
| The hedge algorithm on a continuum W Krichene, M Balandat, C Tomlin, A Bayen International Conference on Machine Learning, 824-832, 2015 | 23 | 2015 |
| A bayesian perspective on residential demand response using smart meter data D Zhou, M Balandat, C Tomlin 2016 54th Annual Allerton Conference on Communication, Control, and …, 2016 | 22 | 2016 |
| On efficiency in mean field differential games M Balandat, CJ Tomlin 2013 American Control Conference, 2527-2532, 2013 | 20 | 2013 |
| Sustainable ai: Environmental implications, challenges and opportunities CJ Wu, R Raghavendra, U Gupta, B Acun, N Ardalani, K Maeng, G Chang, ... Proceedings of Machine Learning and Systems 4, 795-813, 2022 | 14 | 2022 |
| Optimizing coverage and capacity in cellular networks using machine learning RM Dreifuerst, S Daulton, Y Qian, P Varkey, M Balandat, S Kasturia, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 13 | 2021 |
| Efficient nonmyopic bayesian optimization via one-shot multi-step trees S Jiang, D Jiang, M Balandat, B Karrer, J Gardner, R Garnett Advances in Neural Information Processing Systems 33, 18039-18049, 2020 | 12 | 2020 |
| Eliciting private user information for residential demand response DP Zhou, M Balandat, MA Dahleh, CJ Tomlin 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 189-195, 2017 | 12 | 2017 |
| Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement S Daulton, M Balandat, E Bakshy Advances in Neural Information Processing Systems 34, 2187-2200, 2021 | 10 | 2021 |
| Minimizing regret on reflexive banach spaces and learning nash equilibria in continuous zero-sum games M Balandat, W Krichene, C Tomlin, A Bayen arXiv preprint arXiv:1606.01261, 2016 | 8 | 2016 |
| Estimating treatment effects of a residential demand response program using non-experimental data DP Zhou, M Balandat, CJ Tomlin 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 95-102, 2017 | 7 | 2017 |
| Minimizing regret on reflexive Banach spaces and Nash equilibria in continuous zero-sum games M Balandat, W Krichene, C Tomlin, A Bayen Advances in Neural Information Processing Systems 29, 2016 | 7 | 2016 |
| Bayesian optimization with high-dimensional outputs WJ Maddox, M Balandat, AG Wilson, E Bakshy Advances in Neural Information Processing Systems 34, 19274-19287, 2021 | 6 | 2021 |
| Multi-objective bayesian optimization over high-dimensional search spaces S Daulton, D Eriksson, M Balandat, E Bakshy arXiv preprint arXiv:2109.10964, 2021 | 6 | 2021 |
Claire TomlinUC BerkeleyVerified email at berkeley.edu
